# https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

import time

# Download training data from open datasets.
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
)

# Download test data from open datasets.
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor(),
)

batch_size = 64

# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

for X, y in test_dataloader:
    print(f"Shape of X [N, C, H, W]: {X.shape}")
    print(f"Shape of y: {y.shape} {y.dtype}")
    break

# Get cpu, gpu or mps device for training.
device = (
    "cuda"
    if torch.cuda.is_available()
    else "mps"
    if torch.backends.mps.is_available()
    else "cpu"
)
print(f"Using {device} device")

# Define model
class NeuralNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=(5, 5), padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU()
        )                                           # out 28*28*16
        self.pool1 = nn.MaxPool2d(2)                # out 14*14*16
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=(3, 3)),
            nn.BatchNorm2d(32),
            nn.ReLU()
        )                                           # out 12*12*32
        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=(3, 3)),
            nn.BatchNorm2d(64),
            nn.ReLU()
        )                                           # out 10*10*64
        self.pool2 = nn.MaxPool2d(2)                # out 5*5*64
        self.fc = nn.Linear(5 * 5 * 64, 10)         # out 10

    def forward(self, x):
        out = self.layer1(x)
        out = self.pool1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.pool2(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train() # Sets the module in training mode.
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)

        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        optimizer.zero_grad() # 将所有优化的torch.Tensor梯度设为零。
        loss.backward()
        optimizer.step() # 执行单个优化步骤（参数更新）。

        if batch % 100 == 0:
            loss, current = loss.item(), (batch + 1) * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]", end='\r')

def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= num_batches
    correct /= size
    print(f"Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")


if __name__ == "__main__":
    model = NeuralNetwork().to(device)
    print(model)

    loss_fn = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

    timeA = time.time()

    epochs = 20
    for t in range(epochs):
        print(f"Epoch {t+1}\n-------------------------------")
        train(train_dataloader, model, loss_fn, optimizer)
        test(test_dataloader, model, loss_fn)
    print(f"Done! {(time.time() - timeA)/60.0:>0.1f} minute")

    torch.save(model.state_dict(), "cnnModel.pth")
    print("Saved PyTorch Model State to model.pth")